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At least 73 records · Page 4

Stochastic fracture generation and thermo-hydro-mechanical modeling in an equivalent continuum framework for enhanced geothermal systems

Enhanced geothermal systems (EGS) involve fracturing low permeability material to establish well connectivity and then injecting and circulating fluid into the fractured subsurface for geothermal power production. Changes in fracture aperture from contraction of the cooling matrix rock may alter network connectivity and risk thermal short-circuiting. Thermo-hydro-mechanical (THM) models are a useful tool to study these processes. However, as fracture networks are complex, and data may be limited, fracture networks in THM models are often stochastically generated. Given reliance on stochastic fracture networks and THM modeling to represent the subsurface and assess productivity of EGS, increased understanding of the influence of such statistically derived fracture networks on flow and heat transport in THM models is needed. Here, a new fracture process model is developed in the reactive transport code PFLOTRAN to stochastically generate fracture families and simulate changes in fracture aperture over time due to temperature changes of the rock matrix. Sixty-four different fracture networks ranging from well to poorly-connected, are modeled in PFLOTRAN with and without mechanical processes (THM vs TH). Results indicate that for well-connected fracture networks, thermal short-circuiting is less of a concern due to the abundance of available alternative flowpaths. For poorly-connected fracture networks, inclusion of mechanical processes showed steep thermal drawdown coincident with increase in fracture aperture along developing colder flowpaths, demonstrating the risk of thermal short-circuiting. Simulations with additional, larger fractures engineered to establish connectivity in a poorly-fractured subsurface, indicate that while stochastic variation of fracture orientation of the background network had limited influence, such variation in the engineered fractures significantly affected flow and heat transport.

Discrete fracture networks (DFN)↗

Semi-interpenetrating polymer network's of polyimides: Fracture toughness

The objective was to improve the fracture toughness of the PMR-15 thermosetting polyimide by co-disolving LaRC-TPI, a thermoplastic polyimide. The co-solvation of a thermoplastic into a thermoset produces an interpenetration of the thermoplastic polymer into the thermoset polyimide network. A second research program was planned around the concept that to improve the fracture toughness of a thermoset polyimide polymer, the molecular weight between crosslink points would be an important macromolecular topological parameter in producing a fracture toughened semi-IPN polyimide.

Hansen, Marion Glenn↗

Detecting and Characterizing Fracture Zones Using a Convolutional Neural Network

This project directly supports the Geothermal Technologies Office (GTO) objectives outlined in the Multi-Year Program Plan (MYPP) by advancing two key research areas: “Exploration and Characterization” and “Data, Modeling, and Analysis.” This project has successfully demonstrated a pre-drilling ability to image and characterize the distribution and connectivity of subsurface faults and fractures, key parameters for identifying permeable pathways that enable geothermal fluids to circulate and produce energy. Specifically, we developed and implemented innovative machine learning methodologies to enhance geothermal exploration. Large-scale faults were detected using a Convolutional Neural Network (CNN), while small-scale fractures were characterized using a novel Double-Beam Neural Network (DBNN). These tools have proven both technically effective and cost-efficient by reducing reliance on expensive exploratory drilling. Through collaboration with our geothermal industry partner, this research has significantly advanced techniques for identifying hidden geothermal systems and extending the productive lifespan of existing geothermal fields. We applied our methods to two geothermal fields—Soda Lake (Nevada) and Lightning Dock (New Mexico)—to identify shallow steam-charged fracture zones and characterize deep faults at depths of 1.5-2 km. The steam zone identified at the Soda Lake geothermal field showed excellent agreement with prior drilling data, validating the effectiveness of our approaches. In addition, the analysis revealed three new prospective drilling targets for further development and verification. The outcomes of this project improve our scientific understanding of geothermal reservoir behavior, enhance exploration efficiency, extend the economic life of existing geothermal plants. Ultimately, these advancements contribute to GTO’s goal of achieving more sustainable, affordable, and data-driven geothermal energy development across the United States.

15 GEOTHERMAL ENERGY↗

Modification of Fractured Rock Permeability Field Using Polymer Foam to Improve Geothermal System Efficiency

The project aims at developing novel methods for blocking high-permeability pathways in geothermal reservoirs to improve heat exchange efficiency. While the project was at DRI, and now when it has been transferred to UNR, the goal of blocking high-permeability pathways is achieved through targeted injection of heat-sensitive polymer foams, particularly foamed epoxy resins, whose activation and curing are triggered by the ambient thermal field.

15 GEOTHERMAL ENERGY↗

Is Europa surface cracking due to thermal evolution

It is proposed that some of the surface features of Europa may be due to processes occurring within a planet in which hydrated silicates are stable. Based on considerations of the mechanical properties of hydrated silicates and ice and the likely thickness of the ice crust (less than 24 km), models are developed for the long, relatively dark lineaments that lie on great and small circles and frequently possess bright medial stripes, and for the complex network of fractures surrounded by cuspate ridges in the antijovian region. The global-scale lineaments are explained in terms of planetary expansion brought about by positive volume changes resulting from the dehydration of the hydrated silicates. The apparently tensile features in the antijovian region are attributed to a region of upwelling in the convecting anhydrous silicate interior surrounded by a region of return flow beneath the hydrated silicate mantle.

Finnerty, A. A.↗

The surface and interior of Phobos

The impact crater Stickney dominates one hemisphere of the Martian moon Phobos; its diameter (11 km) is about half the size of the body (19 x 22 x 27 km). Besides demarking a threshold between cratering and catastrophic disruption, this impact reveals a great deal about the target's interior. Because Phobos has an unusually low density yet exhibits no direct evidence for volatiles such as water ice, it has been supposed that it sequesters volatiles in the deep interior, or that it is made of some exotic substance, or that it is a loosely-aggregated rubble pile. The network of fracture grooves created by the Stickney impact constrain which, if any, of these models accord with observation.

Asphaug, E.↗

Venus volcanism: A global overview from Magellan data

A preliminary analysis of a global survey of Magellan data covering over 90 percent of the surface and designed to document the characteristics, location, and dimensions of all major volcanic features on Venus has revealed over 1660 landforms and deposits. These include over 550 shield fields (concentrations of small volcanos less than 20 km in diameter), 274 intermediate volcanos between 20 and 100 km diameter with a variety of morphologies, 156 large volcanos in excess of 100 km diameter, 86 calderalike structures independent of those associated with shield volcanos and typically 60-80 km in diameter, 175 coronae (annulus of concentric ridges or fractures), 259 arachnoids (inner concentric and outer radial network pattern of fractures and ridges), 50 novae (focused radial fractures forming stellate patterns), and 53 lava flood-type flow fields, and 50 sinuous lava channels (all of which are in excess of 10(exp 2)-10(exp 3) km in length). The near-global coverage of Magellan data analyzed in this study confirms and extends the results of earlier observations that showed that volcanism is a widespread and significant process on the surface of Venus for the period of time in the presently observed record (less than about the last one billion years).

Head, James W., III↗

Inferring fracture dilation and shear slip from surface deformation utilising trained surrogate models

An important task in energy and CO 2 storage (sequestration) in the subsurface is to verify that the surrounding fractures and faults are not activated, acting as leakage pathways. This is achievable through effective and efficient Measurement, Monitoring and Verification (MMV) plans. In this work, two surrogate models are trained to captures dilation (opening) and shear deformation of fractures, and the associated surface deformation. The trained surrogate model, based on conditional Generative-Adversarial Networks (cGAN) receives fracture apertures from dilational fractures together with fracture slips from shear fractures and predicts the combined surface deformation. An inversion algorithm based on Bayesian framework is proposed to identify the geometry of both types of fractures, as well as volume of dilational fractures and deformation moment induced by shear fractures, all from the measured surface deformation data. The inversion algorithm utilises the Differential Evolution (DE) optimisation technique that has the superior performance in finding the global minimum of cost function. The proposed surrogate-assisted inversion successfully inferred the unknown dip, dip direction and the volume of the dilational fractures as well as the induced deformation moment in shear fractures. The model was further tested for the inversion of a field hydraulic fracturing tilt dataset applying different scenarios with varying unknowns to show the model's performance, as well as incorporating shear deformation for better match with the observed data.

Dilation and shear↗

Use of satellite pictures for determining major shield fractures relevant for ore prospecting, northern Finland

The author has identified the following significant results. A combined analysis of LANDSAT 1 imagery, aeromagnetic and other maps, and aerial photos has revealed a dense network of bedrock fractures in northern Finland. They form several fracturing zones, which obviously represent surficial manifestations of major fractures. The fractures follow, in general, the eight main trends of crustal shear characteristics of the Baltic Shield, but show distinct deviations from them in detail. The major fracture zones divide the bedrock into a mosaic of polygonal blocks, which in many cases coincide with the main rock units of the area and are characterized by different patterns of internal fracturing. Known mineralizations show a tendency to concentrate along the fracture zones. Optical filtering of original LANDSAT images might provide a rapid tool for the analysis of major structural trends in extensive areas such as shields or entire continents.

Tuominen, H. V.↗

Insights into elevated temperature tensile deformation mechanisms and kink banding in additively manufactured tungsten

Additive manufacturing (AM) potentially enables fabrication and repair of plasma facing components (PFCs) of tungsten. However, deformation mechanisms in AM-fabricated tungsten under service-relevant thermomechanical conditions remain unknown majorly due to challenges in achieving crack-free W. This study reveals the deformation mechanisms operative under tension at 800 °C and 1200 °C in the electron beam melting powder bed fusion (EBM-PBF) fabricated W. Textured columnar grains with mixed <001 >/<111 >|| build-direction were observed. Strong anisotropy in tensile properties persisted across test temperatures, and all specimens exhibited extensive deformation-induced banding phenomena. Grain boundary character analysis of the deformed specimens indicated that these deformation bands were kink bands with tilt grain boundaries prominently present at the band-matrix interface. Interestingly, the material within the kink bands rotated toward higher resolved shear stress, providing insights into the origins of kink band formation in body-centered cubic refractory metals. Intragranular misorientation axis analysis revealed that plasticity was dominated by {110} <111 > slip at 800 °C, whereas additional (213)[11⁢̄1] and (112)[11⁢̄1] slip systems activated at 1200 °C. A dense low-angle boundary networks observed near fractured surface indicated the onset of dynamic recrystallization. Results reveal plasticity governing mechanisms in AM-fabricated W under power plant-relevant conditions, and provide insights into kink band attributes in refractory metals.

Mayes, Riley [ORNL] (ORCID:000900089307010X)↗

Tough poly(arylene ether) thermoplastics as modifiers for bismaleimides

Several aspects of research on thermoplastics as toughness modifiers are discussed, including the contribution of the backbone chemistry and the concentration of the poly(arylene ether) thermoplastic to fracture toughness, influence of the molecular weight of the poly(arylene ether) thermoplastic on neat resin fracture toughness, and the morphology of the thermoplastic modified networks. The results show that fracture toughness of brittle bismaleimide resins can be improved significantly with poly(arylene ether) thermoplastic levels of 20 percent by weight, and that high molecular weight poly(arylene ether) based on bisphenol A provides the highest degree of toughening. Preliminary composite evaluation shows that improvements in neat resin toughness translate into carbon fabric composite.

Stenzenberger, H. D.↗

Deep Learning–Assisted Multiobjective Optimization of Geological CO 2 Storage Performance under Geomechanical Risks

In geological CO 2 storage, designing the optimal well control strategy for CO 2 injection to maximize CO 2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO 2 plume migration, the highly nonlinear nature of geomechanical responses to rock-fluid interaction, and the high computational cost associated with coupled flow and geomechanics simulations. In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: maximize total CO 2 storage while minimizing geomechanical risks by adjusting the injection schedules within bounded constraints. The geomechanical risks are primarily driven by injection-induced pressure build-up, which is characterized by ground displacement and the induced microseismicity. We used the Fourier neural operator (FNO)-based deep learning model to construct surrogate models, replacing the time-consuming coupled flow and geomechanics simulations for evaluating the aforementioned objective functions. The developed surrogate models have been incorporated into a multiobjective optimization framework through a genetic algorithm to reduce the computational burden. The proposed optimization framework reduces the computational cost from approximately 2,400 hours, when using objective function evaluations based on physics-based simulations, to around 20 minutes. A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, reducing the microseismicity potential and the vertical displacement. This Pareto front highlights the optimal trade-offs between CO 2 storage amount, safety, and ground displacement, emphasizing the need for careful optimization and management of injection strategies to achieve a balanced outcome. The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as objective functions into the CO 2 storage optimization workflow to mitigate the potential risk of induced microseismicity and ground displacement. Second, we leverage the FNO-based surrogate models to optimize a real-field CO 2 storage operation.

42 ENGINEERING↗

Coupled Investigation of Fracture Permeability Impact on Reservoir Stress and Seismic Slip Behavior (Final Technical Report)

Enhanced Geothermal Systems (EGS) produce clean energy by circulating fluid through hot rock deep underground and bringing that heat to the surface to generate electricity. For this process to work reliably, fluids must be able to move efficiently through networks of natural or engineered fractures in the rock. Enhancing and maintaining subsurface permeability over time is essential for sustainable energy production. However, fluid injection changes the underground temperature, pressure, rock stress, and chemistry, which can alter permeability and sometimes trigger earthquakes. Predicting these interconnected processes remains a key challenge. To address this, we combined high-temperature laboratory experiments with high-fidelity simulations to better understand how fractures in geothermal reservoirs evolve over time. Our experiments measured how fractures respond to stress, slip, slip rate, and chemical reactions under geothermal conditions. These data were integrated into coupled thermal-hydrological-mechanical-chemical and earthquake (THMC+E) models tailored to the Utah FORGE site. The validated modeling framework improves predictions of reservoir performance and seismic response and helps guide operational decisions. This work reduces technical risk and strengthens the scientific foundation needed to make geothermal energy a reliable and scalable clean energy resource.

15 GEOTHERMAL ENERGY↗

Generalizable machine learning potentials for quantum-accurate predictions of non-equilibrium behavior in 2D materials

Machine learning interatomic potentials (ML-IAPs) are emerging as transformative tools in materials modeling, promising quantum-level accuracy at a fraction of the computational cost. However, their ability to generalize beyond equilibrium configurations and to reliably capture defect- and temperature-driven behavior remains underexplored. Here, we develop and benchmark two state-of-the-art ML-IAPs, Spectral Neighbor Analysis Potential (SNAP) and Allegro, on a comprehensive dataset for monolayer MoSe₂. Using density functional theory (DFT) as the reference, we evaluate their performance in capturing stress–strain behavior, phase transition energetics, defect evolution, edge stability, and fracture toughness. Allegro, a deep equivariant neural network potential, surpasses both SNAP and the classical Tersoff potential in accuracy, efficiency, and transferability. Importantly, both ML potentials accurately reproduce experimental fracture measurements and ab initio predictions of inversion domain formation—phenomena well beyond their training sets. Our findings establish ML-IAPs as viable replacements for traditional force fields in the study of non-equilibrium mechanical phenomena, enabling large-scale, high-fidelity simulations in 2D materials and beyond. In conclusion, this work provides a broadly applicable framework for data-driven modeling of structural and functional transformations under extreme conditions.

2D materials↗

Diagenesis is key to unlocking outcrop fracture data suitable for quantitative extrapolation to geothermal targets

Exceptionally large, well-exposed sandstone outcrops in New York provide insights into folds, deformation bands, and fractures that could influence permeability, heat exchange, and stimulation outcomes of geothermal reservoir targets. Cambrian Potsdam Sandstone with <5% porosity contains decimeter-scale open, angular-limbed monoclines <0.5 km apart with associated low-porosity mm-wide cataclastic deformation bands. Crossing and abutting relationships among sub-vertical opening-mode fractures show four chronological Sets A–D, striking NNW, NE, NW, and ENE, respectively. Fracture lengths and heights range from millimeters to tens of meters. Sets A and C macro-fractures, and possibly B and D, contain quartz deposits. All sets have abundant associated quartz cemented microfractures that also record set orientations and crosscutting relations. Quartz cement deposits—evidence of diagenesis—are the key to identifying attributes of outcrop fractures suitable for extrapolation to geothermal targets in sandstones because they show which fractures formed in the subsurface. Set A fluid inclusion homogenization temperatures (120°C–129°C) are compatible with fracture at >3 km depth. Fractures are stiff and those ≥0.05 mm (Set C) and ≥0.1 mm (Set A) are open and potentially conducive to flow. Sets A and D are abundant in outcrops with close fracture spacing—0.18 m and 0.68 m, respectively—and define a rectangular connectivity network dominated by crossing and abutting X and Y nodes. Set A aperture distributions follow a power law with slope –0.8 up to 0.15 mm; other sets have lognormal distributions. Set A and D microfractures are weakly clustered, while macro-fractures commonly have 1D anticlustered (regular or periodic) arrangements at shorter length scales (<0.2 m). Sub-horizontal fractures are barren and may have formed near the surface. Fracture heights, lengths, and spatial arrangements show good trace connectivity but low open connectivity. For geothermal applications, outcrop results predict low initial well-test permeabilities owing to quartz disconnecting open fractures, but stimulation of closely spaced microfractures and partly open macro-fractures could yield high surface area for heat exchange. Quantitative extrapolation of key fracture attributes like abundance, orientation, spatial arrangement, length, and open fracture connectivity is possible from outcrops to fractured reservoirs if differing thermal histories and diagenesis are accounted for.

02 PETROLEUM↗